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Abstract

An automated detection method and system improve the diagnostic procedures of radiological images containing abnormalities, such as lung cancer nodules. The detection method and system use a multi-resolution approach to enable the efficient detection of nodules of different sizes, and to further enable the use of a single nodule phantom for correlation and matching in order to detect all or most nodule sizes. The detection method and system use spherical parameters to characterize the nodules, thus enabling a more accurate detection of non-conspicuous nodules. A robust pixel threshold generation technique is applied in order to increase the sensitivity of the system. In addition, the detection method and system increase the sensitivity of true nodule detection by analyzing only the negative cases, and by recommending further re-assessment only of cases determined by the detection method and system to be positive. The detection method and system use multiple classifiers including back propagation neural network, data fusion, decision based pruned neural network, and convolution neural network architecture to generate the classification score for the classification of lung nodules. Such multiple neural network architectures enable the learning of subtle characteristics of nodules to differentiate the nodules from the corresponding anatomic background. A final decision making then selects a portion of films with highly suspicious nodules for further reviewing.

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